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Exam Detail:
The MongoDB Certified DBA Associate (C100DBA) exam is designed to assess the knowledge and skills of individuals in administering MongoDB databases. The certification validates the candidate's understanding of MongoDB architecture, configuration, monitoring, and performance optimization. Here are the details of the C100DBA exam:
- Number of Questions: The C100DBA exam typically consists of multiple-choice questions and practical scenarios. The exact number of questions may vary, but it generally ranges from 60 to 90 questions.
- Time Limit: The time allocated to complete the C100DBA exam is usually around 90 minutes. However, the duration may vary depending on the specific exam requirements and the exam delivery platform.
Course Outline:
The C100DBA exam covers the following key topics:
1. MongoDB Basics:
- Overview of MongoDB and NoSQL databases.
- Understanding MongoDB architecture and components.
- Installation and configuration of MongoDB.
2. Data Modeling and Indexing:
- Designing MongoDB schemas and collections.
- Creating and managing indexes for optimal query performance.
- Understanding data normalization and denormalization.
3. Querying and Aggregation:
- Querying MongoDB using the MongoDB Query Language (MQL).
- Aggregating data using the MongoDB Aggregation Framework.
- Optimizing query performance and using query hints.
4. Replication and High Availability:
- Configuring replica sets for data redundancy and high availability.
- Managing replication and failover processes.
- Performing backups and restores.
5. Sharding and Scalability:
- Scaling MongoDB databases using sharding.
- Configuring and managing sharded clusters.
- Monitoring and optimizing sharding performance.
6. Security and Authentication:
- Implementing security measures and access controls.
- Configuring authentication and user roles.
- Securing data at rest and in transit.
Exam Objectives:
The objectives of the C100DBA exam are as follows:
- Assessing the candidate's understanding of MongoDB architecture, components, and installation.
- Evaluating the candidate's knowledge and skills in data modeling and index optimization.
- Testing the candidate's ability to query and aggregate data using MongoDB Query Language and Aggregation Framework.
- Verifying the candidate's proficiency in configuring replication, high availability, and backups.
- Assessing the candidate's knowledge of sharding, scalability, and performance optimization.
- Testing the candidate's understanding of MongoDB security measures and authentication mechanisms.
Exam Syllabus:
The C100DBA exam covers the following topics:
1. MongoDB Basics
2. Data Modeling and Indexing
3. Querying and Aggregation
4. Replication and High Availability
5. Sharding and Scalability
6. Security and Authentication
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MongoDB
C100DBA
MongoDB Certified DBA Associate
http://killexams.com/pass4sure/exam-detail/C100DBA Question: 38
Which are the ONLY ways to project portions of an array?
A . $slice
B . $
C . All of the above
D . $ elemMatch Answer: C Question: 39
Which operations add new documents to a collection?
A . Create
B . update
C . insert
D . delete Answer: A,C Question: 40
The following aggregation option is used to specify the specific fields that needs to be passed to the next stage of the
aggregation pipeline:
A . $project
B . $aggregate
C . $match
D . $group Answer: A Question: 41
Which of the following is true about sharding?
A . Creating a sharded key automatically creates an index on the collection using that key
B . We cannot change a shard key directly/automatically once it is set up
C . A sharded environment does not support sorting functionality since the documents lie on various mongod instances
D . Sharding is enabled at the database level Answer: B Question: 42
MongoDB is
A . None of the above
B . Object-oriented DBMS
C . Relational DBMS
D . Document-oriented DBMS Answer: D Question: 43
Using an arbiter allows one to easily ensure an odd number of voters in replica sets.
Why is this important?
A . To help in disaster recovery
B . To protect agains network partitions
C . To enable certain read preference settings
D . To add greather redundancy
E . For more efficient backup operations Answer: B
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https://killexams.com/exam_list/MongoDBBest Big Data Certifications
Today’s organizations are looking for better ways to pull the information they need from massive volumes of data available to them. Big data system administrators store, manage and transfer large sets of data, making them amenable to analysis. Data analytics is the practice of examining the raw data to identify patterns and draw conclusions. Business intelligence involves the collection and organization of information to report on business activities, often pulling data from those very sets.
Along with the surge in big data interest comes a growing number of certifications to recognize the necessary skills in working with enormous data sets. The target audience is IT professionals with a background in analytics, data mining, business intelligence or data management, along with a knack for and interest in mathematics and statistics. Big data engineers are in reasonably high demand and usually command salaries of $101,000 or more, where data scientists average about $128,500 in salary.
We took a detailed look at certifications from INFORMS, Microsoft, MongoDB, Oracle and SAS. Check out the results from our informal search of various job boards to see which certifications employers really want. However, these results are a snapshot from a specific day, time and geography, so they may not reflect genuine job demands in your locality when you search for yourself.
Job board search results (in alphabetical order, by certification)
>MCSE: Data Management and Analytics (Microsoft)
377
>271
31
320
>999
>MongoDB Certified DBA
12
13
8
5
38
>MongoDB Certified Developer
104
124
49
31
308
Oracle Business Intelligence
323
>503
713
194
1,733
SAS Certified Data Scientist
88
11
10
18
228
*Certified Analytics Professional
CAP: Certified Analytics Professional
INFORMS is a membership-based association aimed at practitioners, researchers and instructors in analytics, as well as operations research and management sciences. The association reports about 12,500 members from nearly 90 countries, most of whom participate in various educational and networking opportunities via their INFORMS membership.
The organization also sponsors the vendor-neutral Certified Analytics Professional (CAP) certification. CAP focuses on seven domains of the analytics process:
Business problem framing
Analytics problem framing
Data
Methodology selection
Model building
Deployment
Model lifecycle management
Candidates must meet rigorous education and experience requirements, obtain confirmation from a supervisor (current or former) of acceptable soft skills, agree to a code of ethics, and pass one computer-based exam.
This credential must be renewed every three years by earning 30 professional development units (PDUs). INFORMS also requires a $100 annual maintenance fee beginning in the fourth year after certification.
CAP facts and figures
>Certification name
Certified Analytics Professional (CAP)
Prerequisites and required courses
Eligibility to sit for exam:
A BA/BS degree or MA/MS degree in an analytics-related area (as approved by INFORMS)
A minimum of 3 years of professional analytics-related experience with an MA/MS degree related to analytics OR 5 years with a related BA/BS degree related OR 7 years with a BA/BS degree or higher that’s not related to analytics
Supervisory confirmation of demonstration of acceptable soft skills
Agreement to code of ethics
>Number of exams
1 exam (100 multiple-choice questions, 3 hours)
Cost per exam
Exam: $495 member, $695 nonmember
Re-examination fee: $300 member, $400 nonmember
Exams administered by Kryterion. Candidates must be approved by INFORMS and pay for the exam before registering with an exam site.
The Certified Analytics Professional Candidate Handbook includes exam courses (the 7 domains), sample test questions and a list of self-study resources. A study guide is available on the CAP website (membership or registration required).
MCSE: Data Management and Analytics
Microsoft is part of the big data mix with its MCSE: Data Management and Analytics certification, which leans heavily toward SQL Server 2016 and emphasizes cloud environments and reporting. This credential replaces the Microsoft Certified Solutions Expert (MCSE): Business Intelligence, which retired on March 31, 2017.
The MCSE: Data Management and Analytics requires candidates to earn an MCSA certification and then pass an elective exam. The MCSA certifications to choose from are MCSA: SQL Server 2012/2014; MCSA: SQL 2016 Database Administration; MCSA: SQL 2016 Database Development; MCSA: SQL 2016 BI Development; MCSA: Machine Learning; MCSA: BI Reporting; and MCSA: Data Engineering with Azure. There are 12 MCSE elective exams, which focus on a variety of cloud, big data analytics and BI topics, with machine learning now added to the MCSA certifications.
To maintain this certification, you must pass an additional elective exam each year. Doing so adds an entry to your transcript that indicates your commitment to staying current on technologies and expanding your skill set.
MCSE: Data Management and Analytics facts and figures
Number of exams
Certification name
MCSE: Data Management and Analytics
Prerequisites and required courses
Prerequisites (1 required):
MCSA: SQL Server 2012/2014
MCSA: SQL 2016 BI Development
MCSA: SQL 2016 Database Administration
MCSA: SQL 2016 Database Development
MCSA: Machine Learning
MCSA: BI Reporting
MCSA: Data Engineering with Azure
Training courses available and recommended for all certifications but not required.
One of the following:
70-473: Designing and Implementing Cloud Data Platform Solutions
70-475: Designing and Implementing Big Data Analytics Solutions
Each exam page has links to instructor-led training, exam prep videos, self-paced training, practice exams and books.
*Exam to be retired on June 30, 2019. Replacement test not yet released but should be available prior to that date.
MongoDB NoSQL certifications
MongoDB is both an open-source, NoSQL document-oriented database and the name of the company providing that technology. Because of its document-oriented NoSQL model, MongoDB is well suited for managing large amounts of loosely structured data, as is so often the case in big data projects.
MongoDB was deemed a leader in Forrester Wave: Big Data NoSQL, Q3 2016, and Gartner selected MongoDB as a challenger in its 2016 Magic Quadrant for Operational Database Management Systems. The database ranks fifth in overall database engine popularity as of March 2019.
The MongoDB NoSQL certification program recognizes developers and operations professionals who can create and run applications on MongoDB. The program offers two associate-level credentials: MongoDB Certified DBA and MongoDB Certified Developer. The company plans to eventually roll out higher-level certifications. The current exams are based on MongoDB V4.0.
MongoDB offers private, instructor-led classroom training as well as free online video training through MongoDB University. Each online course typically runs for 3-7 weeks and features video lectures, quizzes, weekly assignments, and a final exam or project. Students may use the forums to interact and address each other’s questions; instructors and teaching assistants monitor forums and jump in when needed.
For database professionals interested in venturing into big data projects and NoSQL databases, the MongoDB certification is certainly a worthwhile goal.
MongoDB NoSQL certification facts and figures
>Certification name
MongoDB Certified DBA, Associate Level
MongoDB Certified Developer, Associate Level
Prerequisites and required courses
None; training recommended but not required
Number of exams
1 exam per credential (multiple-choice questions, 90 minutes; delivered online through MongoDB’s proctoring partner)
MongoDB University offers public and private training, as well as free online courses. Each exam webpage includes a study guide and links to documentation, presentations and exam study sessions (if available).
Oracle Business Intelligence certification
Oracle has one of the largest certification programs in the world, and it has granted more than 1 million Oracle and Sun certifications.
Oracle offers its Business Intelligence (BI) certifications across several applications and platforms, such as Business Intelligence Applications 7 for CRM, Business Intelligence Applications 7 for ERP, and Business Intelligence Foundation 11g. To narrow down the field, we focused on the Oracle Business Intelligence Foundation Suite 11g Certified Implementation Specialist credential.
Candidates for this certification are intermediate-level professionals (architects, analysts, developers and administrators) who work with Oracle Business Intelligence Suite solutions, performing tasks such as installing, building, querying, configuring, and managing the platform and BI dashboards.
Although anyone can attempt the exam, the certification is designed for members of the Oracle Partner Network who sell and deploy the technology.
New exams and training courses based on Oracle BI 12c are also becoming available as those courses and exams are released to the public.
Oracle Business Intelligence certification facts and figures
Certification name
Oracle Business Intelligence Foundation Suite 11g Certified Implementation Specialist
Prerequisites and required courses
None.
Recommended:
Number of exams
1 exam: 1Z0-591 Oracle Business Intelligence Foundation Suite 11g Essentials (75 multiple-choice questions, 120 minutes, 63% passing score)
Cost per exam
$245, administered by Pearson VUE. Exam vouchers may be purchased from Oracle University or Pearson VUE; Oracle Testing ID required to register; Oracle Partner Network members also need their OPN Company ID.
Training is available, including classroom, live virtual class, training on demand, self-study, learning streams and practice exams. Additional exam preparation materials are listed on the exam webpage.
SAS Certified Data Scientist
SAS is a multibillion-dollar global corporation that specializes in business analytics software and services. The company’s well-honed certification program offers nine credentials across programming, information and data management, advanced analytics, and business intelligence.
Candidates for the Data Scientist certification should have in-depth knowledge of and skills in manipulating big data using SAS and open-source tools, using complex machine learning models, making business recommendations, and deploying models. Candidates must pass five exams to earn the SAS Certified Data Scientist credential.
SAS doesn’t require candidates to take certification courses as a prerequisite for exams, but the company offers an official curriculum for those who have the budget and need the extra preparation.
SAS “versioned” credentials, such as the SAS Certified Data Scientist Using SAS 9, do not expire. However, SAS may retire exams as new or enhanced software is released.
SAS Certified Advanced Analytics Professional Using SAS 9
See a list of courses that support Big Data and Advanced Analytics on the SAS Academy for Data Science webpage. Recommended Data Scientist training: SAS Certified Data Scientist (e-learning, $4,400; live instructor-led in Cary, North Carolina; 12 weeks, $16,000)
SAS Big Data Preparation, Statistics and Visual Exploration
SAS Big Data Programming and Loading
SAS Certified Advanced Analytics Professional Using SAS:
Predictive Modeling Using SAS Enterprise Miner 13 or 14
SAS Advanced Predictive Modeling
SAS Text Analytics, Time Series, Experimentation and Optimization
Exams include multiple-choice, short-answer and interactive questions (interactives are in simulated SAS environment). Exams available through Pearson VUE.
List of exam preparation training and materials available on individual exam pages. Free sample questions available. Practice exams ($500) available through SAS and Pearson VUE.
Beyond the top 5: More big data certifications
It was a real challenge to whittle down this year’s list of popular big data certifications. The Cloudera Certified Professional Data Engineer (CCP Data Engineer) missed a spot in the top five by a small margin. The company also offers CCA Spark and Hadoop Developer and CCA Data Analyst certifications. Given the company’s leadership status in software and services based on Hadoop, the CCP Data Engineer certification is worth your consideration.
Check out Stanford’s Data Mining and Applications graduate certificate. Although it’s not a vendor-neutral or vendor-specific certification (it’s a college certificate), TechExams forum members like it in terms of popularity and job prospects. The certificate requires completion of three courses (nine college units) at a cost of $11,340 to $12,600. The Microsoft Professional Program (MPP) currently offers four tracks for online education with course completion certificates, including two relevant tracks: one on data science, the other on big data. These are worth checking out as well, and significantly cheaper than the Stanford certificate program.
There are many more. A quick Google search every month or two should reveal a growing list of big data, data analytics and BI certifications, and we think one or more of these is a smart way to spend your certification dollars.
Thu, 09 Nov 2023 10:01:00 -0600entext/htmlhttps://www.businessnewsdaily.com/10754-best-big-data-certifications.htmlBuilding modern applications faster: MongoDB’s Sahir Azam on innovation in the AI era
Presented by MongoDB
We live in exciting times for app developers: the advent of democratizing innovations like generative AI (gen AI) and AI-powered coding assistance will lead to an explosion of new applications. Indeed, IDC predicts that over 750 million cloud-native applications will be created by 2025. But for many organizations, maintaining a regular cadence of competitive new products and services remains a challenge.
“On the one hand, organizations are under constant pressure to innovate and differentiate — and that pressure has increased because of generative AI and how disruptive or advantageous it could be for their business,” said Sahir Azam, chief product officer at MongoDB. “Yet, the cost of capital has gone up significantly. Teams are being asked to do this with fewer resources, more efficiency, and for less cost. So, there’s a real tension between the market disruption with gen AI on one hand, and cost-saving pressure and economic headwinds on the other. Balancing those two is top of mind.”
What’s more, developers are in short supply. As a result, it’s crucial that these valuable resources focus on solving their organization’s core challenges, rather than dealing with the complexity of traditional relational database systems. Prioritizing the developer mission is the best way for organizations to stay competitive, as well as the vendors they partner with.
“And that’s why we’re making sure we build technology solutions that are delightful for developers and serve their needs,” Azam added. “But we’re also supporting the most mission-critical, scalable and secure applications in the world.”
VentureBeat spoke with Azam about what organizations are prioritizing as they work to modernize their stacks, from ways AI is transforming the development process from the ground up, to revolutionizing the end user experience and tackling sprawl.
Moving faster with generative AI
Advances in AI, and generative AI in particular, are the biggest news in tech today. Developers and organizations are especially excited about the new AI-powered tools designed to increase productivity. These include everything from a chatbot that answers coding questions, to code generation assistants like Amazon CodeWhisperer and GitHub Copilot.
Azam shared some of the ways MongoDB is investing in AI. For one, he explained, the company has embedded AI into its developer tooling to make it easier for developers to write MongoDB code and queries according to the company’s best practices. MongoDB has also partnered with some of the major hyperscalers — the large-scale data centers that offer massive, on-demand computing resources. These partnerships are focused on optimizing large language model (LLM) training with internal knowledge of MongoDB’s own resources, including documentation, best practices and knowledge bases.
The AI boom also means tools are emerging to support an array of AI use cases. For instance, developers using public APIs like OpenAI and Azure AI need a tool to help them use their proprietary data to better customize their results — and RAG, or Retrieval-Augmentation Generation, was born. And for companies that build and train their own models, the vector database has emerged. Vector databases make it easier for machine learning models to remember previous inputs, making power search, recommendations and text generation use cases more effective.
“For most organizations, the challenge in bringing on these tools also means a whole new technology partner and brand-new technology to validate,” Azam explained. “Making sure it’s secure, stable, performant and so on puts major pressure on IT leaders — and adds yet more tech sprawl. To counter that challenge, we’ve focused on enabling vector database capability out of the box.”
For example, with Atlas Vector Search developers can build AI-powered experiences while accessing all the data they need through a unified and consistent developer experience. Because Atlas Vector Search is built on the MongoDB Atlas developer platform, customers can leverage it without the burden of finding, buying, installing and managing yet another component.
Other AI advances under MongoDB’s belt include new LLM capabilities in MongoDB Compass, which aid developers in MongoDB query writing, speeding up the development process and making sure the code is more accurate. Azam shared that they’ve also integrated gen AI into Atlas Charts, which helps build charts and graphs for the application’s dashboard so that developers can now use natural language to automatically generate queries.
“Typically, you would have to know MongoDB’s query language to generate those beautiful charts and graphs that you want to build in your app or put on your dashboard for your business to look at,” said Azam. “Now you can use natural language to automatically generate the query.”
Finally, MongoDB has begun to implement AI capabilities into its Relational Migrator tool, which significantly reduces the high cost of modernizing legacy. It analyzes the legacy database and then automatically generates new data schema and code to migrate to MongoDB Atlas, with no downtime required. From there, it generates optimized code for working with data in the new, modernized application.
Consolidating costs and tackling technology sprawl
After the wave of digital transformation that marked the past few years, organizations are now taking stock of their vendor relationships. Leaders see how overlapping vendor agreements are leading their teams to spend more time on maintenance than delivering business value.
“We’ve been through the era of the pandemic and a looser monetary policy where it was easy for organizations to spend a lot on technology, leverage whatever sprawl of tools they might have, even if they’re overlapping, even take on the cost and tax of integrating all those things together,” said Azam. “We’re now seeing organizations looking to consolidate costs with less vendors who can provide more capabilities so that they can save time and effort operationally.”
This is exactly why MongoDB has put a major focus on enabling these business needs.
“The developer data platform strategy has been an expansion of what MongoDB has been up to from day one, which is getting the data out of the way of developers building modern applications,” he explained. “With one interface, one language to learn, one environment, developers have what they need to build today’s applications faster, with significantly less sprawl.”
As a result, organizations spend less money and developers are more productive. They’re able to build any kind of application, and gain the flexibility to leverage multiple clouds, whether for differentiation or pricing benefits, or even run apps in their own data center.
The transformation of end-user experiences
“Every organization wants to be defined by the customer experiences they provide, and increasingly those customer experiences are driven by software,” Azam said. “MongoDB makes it easy for organizations to move fast, and to take an idea from inception to a globally scalable application that can serve those millions of users more easily than any other platform.”
On top of that, MongoDB does it in a truly multi-cloud way, which means a developer can build an application in a customer’s data center or run across all major public cloud platforms simultaneously when necessary (such as for regulatory reasons). Organizations can work with multiple infrastructure providers, and as necessary, take advantage of each provider’s differentiated services more easily, all with the flexibility of controlling and managing their data no matter where it needs to run.
Notably, Azam explained, MongoDB is the only company that’s combined all that complexity into a single developer data platform, not just for a single component of the application or database stack.
“If an organization is betting on a technology that’s in the data space, it’s likely a decision that they’ll live with for years, if not decades,” Azam said. “It’s incumbent on them to find technology that their developers love, that can help recruit talent but can also scale with the organization.”
Ready to invest in your developers by giving them the tools, technology and support they need?Start here.
Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.Wed, 15 Nov 2023 15:40:00 -0600VB Staffen-UStext/htmlhttps://venturebeat.com/ai/building-modern-applications-faster-mongodbs-sahir-azam-on-innovation-in-the-ai-era/Code Companions - The Rise Of The Automated Developer
NEW YORK, NY - FEBRUARY 13: A "love lock" is viewed on the Brooklyn Bridge, one of thousands that ... [+]have been placed along the bridge recently on February 13, 2013 in New York City. The phenomenon has gained followers in exact years as couples seek to publicly mark weddings, engagements, and anniversaries in a permanent way. Besides New York, "love locks" can be found on public monuments and bridges in Venice, St. Petersburg and Paris amongst other cities. (Photo by Spencer Platt/Getty Images)
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Everything is automated. Okay, not everything is automated yet, but we are applying automation controls to every fabric of our society from auto-sensing driver alerts in newer smart vehicles (and that’s not even the driverless or electric ones), to automated floor vacuums and onward to smart refrigerators that sense tags on products to alert us when they are past their use-by date.
Some of this automation is ‘simply’ computerization, some of it is smart device edge computing happening across the Internet of Things, some of it is ‘traditional’ predictive Artificial Intelligence (AI) and some of it is this year's darling technology, generative AI (gen-AI) drawing creativity from the Large Language Models (LLMs) and vector databases that feed and serve it.
The automated programmer
If all this automation is happening around us, created by software application development engineers and their colleagues in systems administration and operations, then shouldn’t we be automating more of the core programming function as well? If users can benefit from automation in the form of Robotic Process Automation (RPA) to take away basic desktop task drudgery and developers can build these automated services, then shouldn’t they be benefiting from ‘automations’ themselves i.e. accelerating controls that make it faster to cut code in the first place? Well, yes, clearly.
But code shortcutting techniques and tools are not new per se; we have had systems in place in the programming universe for decades, with libraries of predefined functions that developers could ‘call’ when required - and then of course the whole era of low-code & no-code has followed suit.
Where we now see this process happening is in cloud-native environments with a new and different set of tools. MongoDB is a company known for its developer data platform, a database with an integrated set of related services and the company is now working with Amazon Web Services, Inc. (AWS) to optimize Amazon CodeWhisperer.
What is Amazon CodeWhisperer?
Amazon CodeWhisperer is a general-purpose machine learning (ML)-powered software code generator that is trained on billions of lines of code. This large code language model (not a real term, but hopefully it illustrates what is happening here) is capable of generating software application code at various levels i.e. it can create code snippets (small reusable blocks of code that can be copied and used when needed) or full software code functions (more complete sections of software that feature algorithmic logic and have a working defined purpose at application runtime) and all this can be done in real-time based on a developer's natural language (typed) comments and base of existing code in a developers’ Integrated Development Environments (IDEs).
AWS points out that any given developer’s personalized recommendations in Amazon CodeWhisperer can vary in size and scope, ranging from a single-line comment to more fully formed outputs.
MongoDB is working with AWS in this vein to provide enhanced suggestions for application development and modernization on MongoDB itself. While Amazon CodeWhisperer already provided support for building applications on MongoDB, developers can now get enhanced suggestions based on highly curated training data and evaluations from MongoDB to use best practices. The companies think this will enable software developers to ‘ideate’ (they mean have ideas, right?), prototype new features and accelerate their work in general.
“Generative AI has the potential to not only revolutionize how end-users interact with modern applications but also how developers build those applications,” said Andrew Davidson, SVP of product at MongoDB. “Collaborating with AWS to train Amazon CodeWhisperer on MongoDB is a step in that direction and developers can now build more quickly and focus on higher-value tasks. With built-in security scanning and the ability to provide source and licensing information for generated code, Amazon CodeWhisperer now provides developers building on MongoDB an experience that will get even better over time.”
As organizations today accelerate the deployment of cloud-native applications, developers want to find ways to reduce repetitive tasks so they can focus on building new applications and shipping new features. IDC estimates that 750 million cloud-native applications will be built in the next two years, and that number will likely increase as enterprises and startups alike take advantage of generative AI for both building applications and reinventing end-user application experiences.
“More and more developers are realizing the potential of generative AI-powered coding companions to transform how work gets done, giving them more time to focus on solving hard problems,” said Deepak Singh, VP of next-gen developer experience at AWS. “Amazon CodeWhisperer already provides an optimized experience when working on common coding tasks and with AWS APIs. By collaborating with MongoDB, we are extending those capabilities to millions of MongoDB developers. We are excited to put Amazon CodeWhisperer in the hands of even more developers to help them tap into the transformative potential of generative AI.”
Coding assistants
MongoDB tells us that developers want to integrate gen-AI-powered coding assistants into their day-to-day workflow to increase their productivity and focus on harder problems. However, these assistants are often trained on publicly available datasets or a company’s own internal data and some tools developers build with may not have high-quality, publicly available code samples included as part of a coding assistant’s training data.
As a result, these coding assistants can provide some support for these tools, but the recommendations may not conform to best practices. While developers have realized the potential benefit of AI-powered coding companions across many tasks, they want these solutions to be further optimized for the tools they use today so they can unlock the full potential of generative AI across their day-to-day work.
“Through this new collaboration to train and evaluate Amazon CodeWhisperer on code and libraries specific to MongoDB, developers can get enhanced suggestions for MongoDB to help them more quickly build and modernize their applications,” notes MongoDB’s Davidson and team. “AWS and MongoDB worked together to train Amazon CodeWhisperer on ‘highly curated content’ and code from MongoDB documentation, detailed use cases, and common tasks with best practices that developers encounter when working with data on MongoDB. As a result, Amazon CodeWhisperer will help developers more quickly write high-quality code when building data aggregations, performing database operations and accelerating the migration of applications to MongoDB for modernization.”
These optimizations are available for five of the most common programming languages used to build with MongoDB, including C#, Go, Java, JavaScript and Python, while also allowing developers to take advantage of core Amazon CodeWhisperer features, including built-in security scanning and a reference tracker that provides information about the origin of code suggestions when it resembles open source training data.
Beyond generalized lustre & finish
In the era of automation, developers are using AI to generate code to create applications faster - and these processes and techniques are happening in a very large number of enterprise software vendors today.
We mentioned low-code & no-code already. Appian, a business process automation software company, has introduced Open AI ChatGPT plugins and other AI features this year with the hope of further accelerating application development for programmers and other less technical team members. The Appian AI Copilot service is an AI assistant that promises to increase developer productivity and provide practical value to accelerate development through generative AI interface creation.
The technology from Appian uses generative AI to convert PDF and other structured forms into digital interfaces and interactive business applications. In addition, developers can create and train custom Machine Learning (ML) models for document and email classification, extract data from PDFs and documents. They can also conduct entity and sentiment analysis to classify text into predefined categories using the Appian AI Skill Designer.
“AI is an essential component of the end-to-end automation that businesses need in order to gain efficiency and market differentiation. Other AI tools are complex and lack data privacy, prohibiting most organizations from gaining value from AI,” said Michael Beckley, CTO and founder, Appian. “We are removing these barriers of complexity so anyone can train custom AI models without special skills, while also ensuring AI training data is secure and compliant with regulations. AI Copilot makes Appian the perfect platform for AI to express application designs while enabling humans to understand and visually refine AI creations. Our low-code design simplifies AI while our robust data fabric gives the needed data governance and security controls.”
The developments here are arguably quite progressive and telling. We heard the term ‘curated content’ twice and that’s obviously for a reason i.e. when we apply general-purpose AI tools to specific jobs, we get a more generalized lustre and finish on the final product, which in this case is software-based data services. This work is focused on using tools that stem from the open AI space and the world of generative intelligence, but bringing them into a defined and corralled space where they can be trained to perform in the most optimal way possible.
Software developers are still in short supply globally and these developments will generally create and support work and jobs, rather than make them redundant or obsolete in any way.
Between open source and cloud-hosted, more proprietary solutions, there's an abundance of AI-powered code-generating tools to choose from. So how does one choose? That's a nuanced question. Beyond stronger performance on particular sets of programming languages or logic problems, there's not a lot to differentiate one code-generating tool from another.
But Amazon's looking to change that.
Today, the company announced that its code-generating tool available through AWS, Amazon CodeWhisperer, has been "optimized" to provide "enhanced" suggestions for app development on MongoDB, the open source database management program. Now, CodeWhisperer can provide better MongoDB-related code recommendations that reflect best practices, Amazon says -- enabling developers to prototype more quickly.
"Regardless of what application a developer is building, they can now get generative AI-powered code suggestions that adhere to MongoDB best practices," Deepak Singh, VP of next gen developer experience at AWS, told TechCrunch via email. "Our joint customers in particular will now benefit from optimized suggestions across both AWS and MongoDB, further accelerating development when building highly scalable, cloud-based applications."
To Strengthen CodeWhisperer's suggestions pertaining to MongoDB, AWS says that it worked with the team maintaining the database -- MongoDB the company -- to train CodeWhisperer's underlying AI model on "highly curated" content and code across C#, Go, Java, JavaScript and Python. The training data came from MongoDB documentation, use cases and common MongoDB tasks like data aggregations and operations), and the MongoDB team took active part in evaluating CodeWhisperer's output during the training process.
"Training AI-powered coding tools is an iterative process, and we're excited about the results we’ve seen so far," Andrew Davidson, SVP of product at MongoDB, told TechCrunch in an email interview. "We'll continue working with the Amazon CodeWhisperer team to further optimize performance and accuracy to provide an even better experience for developers building applications with MongoDB Atlas on AWS."
Amazon and MongoDB have a long history together, having collaborated on the launch of MongoDB Atlas, a fully managed MongoDB service on AWS, around seven years ago. So the CodeWhisperer collaboration doesn't come as a complete surprise. But I'm curious to see what sort of precedent it establishes in the commercial code generation space.
For example, can we expect AWS to work with other vendors on codebased-specific optimizations to CodeWhisperer in the future? If so, will money be involved -- and did money change hands between MongoDB and Amazon for this optimization, for that matter? AWS and MongoDB deny it.
Then, there's the potential legal ramifications to consider. Microsoft, GitHub and OpenAI are currently being sued in a class action motion that accuses them of violating copyright law by allowing Copilot, GitHub's own code-generating tool, to regurgitate licensed code snippets without providing credit. Amazon, perhaps, is laying the groundwork to avoid the same fate.
In any case, I wonder whether rivals like GitHub will form their own relationships with vendors to respond to Amazon's CodeWhisperer improvements. Amazon clearly sees the MongoDB tie-in it as a unique selling point -- and complementary to its several other MongoDB products.
I'd expect not anytime soon -- if it ever. Copilot, while reportedly a money loser, is in a position of strength user-base-wise, with well over a million paying individual customers and more than 37,000 enterprise clients. Then again, stranger things have happened in the generative AI space -- lawsuit-driven, competition-spurred or otherwise.
Mon, 06 Nov 2023 00:00:00 -0600en-UStext/htmlhttps://www.yahoo.com/lifestyle/amazons-code-generating-tool-gets-140027058.htmlMongoDB Partners with AWS to Boost CodeWhisperer with MongoDB-Specific Enhancements
NEW YORK, Nov. 7, 2023 — MongoDB, Inc. and Amazon Web Services, Inc. (AWS), announced that the two companies are collaborating to optimize Amazon CodeWhisperer to provide enhanced suggestions for application development and modernization on MongoDB’s industry-leading developer data platform that millions of developers and tens of thousands of customers rely on every day for business-critical applications.
Trained on billions of lines of Amazon and publicly available code, Amazon CodeWhisperer is an AI-powered coding companion from AWS that generates code suggestions based on natural-language comments or existing code in developers’ integrated development environments (IDEs). Working together with AWS, MongoDB provided curated training data for MongoDB use cases and took part in the evaluation of Amazon CodeWhisperer outputs throughout the training process to promote high-quality code suggestions. While Amazon CodeWhisperer already provided support for building applications on MongoDB, developers can now get enhanced suggestions that reflect best practices, allowing developers to ideate more quickly, rapidly prototype new features, and accelerate application development.
“Generative AI has the potential to not only revolutionize how end-users interact with modern applications but also how developers build those applications,” said Andrew Davidson, SVP of Product at MongoDB. “Collaborating with AWS to train Amazon CodeWhisperer on MongoDB is a step in that direction, and developers can now build more quickly and focus on higher-value tasks. With built-in security scanning and the ability to provide source and licensing information when suggestions resemble publicly available open source training data, Amazon CodeWhisperer now provides developers building on MongoDB a unique experience that will get even better over time.”
“More and more developers are realizing the potential of generative AI-powered coding companions to transform how work gets done, giving them more time to focus on solving hard problems,” said Deepak Singh, VP of Next Gen Developer Experience at AWS. “Amazon CodeWhisperer already provides an optimized experience when working on common coding tasks and with AWS APIs. By collaborating with MongoDB, we are extending those capabilities to millions of MongoDB developers. We are excited to put Amazon CodeWhisperer in the hands of even more developers to help them tap into the transformative potential of generative AI.”
As organizations today accelerate deployment of cloud-native applications, developers want to find ways to reduce repetitive tasks so they can focus on building new applications and shipping new features. IDC estimates that 750 million cloud-native applications will be built in the next two years, and that number will likely increase as enterprises and startups alike take advantage of generative AI for both building applications and reinventing end-user application experiences.
Developers want to integrate generative AI-powered coding assistants into their day-to-day workflow to help them increase their productivity and focus on harder problems. However, these assistants are often trained on publicly available datasets or a company’s own internal data, and some tools developers build with may not have high-quality, publicly available code samples included as part of a coding assistant’s training data. As a result, these coding assistants can provide some support for these tools, but the recommendations may not conform to best practices. While developers have realized the potential benefit for AI-powered coding companions across many tasks, they want these solutions to be further optimized for the tools they use today so they can unlock the full potential of generative AI across their day-to-day work.
Through this new collaboration to train and evaluate Amazon CodeWhisperer on code and libraries specific to MongoDB, developers can get enhanced suggestions for MongoDB to help them more quickly build and modernize their applications. AWS and MongoDB worked together to train Amazon CodeWhisperer on highly curated content and code from MongoDB documentation, detailed use cases, and common tasks with best practices that developers encounter when working with data on MongoDB. As a result, Amazon CodeWhisperer can help developers more quickly write high-quality code when building data aggregations, performing database operations, and accelerating migration of applications to MongoDB for modernization.
These optimizations are available for five of the most common programming languages used to build with MongoDB, including C#, Go, Java, JavaScript, and Python, while also allowing developers to take advantage of core Amazon CodeWhisperer features, including built-in security scanning and a reference tracker that provides information about the origin of a code suggestion when it resembles open source training data.
Amazon CodeWhisperer is free for individual developers with no qualifications or time limits for generating code, so the entire MongoDB community can start taking advantage of Amazon CodeWhisperer’s enhanced suggestions. To get started, developers simply install the Amazon CodeWhisperer extension for their preferred IDE, provide an AWS Builder ID, and begin using the service for code completion and generation. Amazon CodeWhisperer now helps reduce the amount of time developers spend creating code for building data-driven applications on MongoDB and will continue to be trained to Strengthen and refine code suggestions.
Headquartered in New York, MongoDB’s mission is to empower innovators to create, transform, and disrupt industries by unleashing the power of software and data. Built by developers, for developers, MongoDB’s developer data platform is a database with an integrated set of related services that allow development teams to address the growing requirements for today’s wide variety of modern applications, all in a unified and consistent user experience. MongoDB has tens of thousands of customers in over 100 countries. The MongoDB database platform has been downloaded hundreds of millions of times since 2007, and there have been millions of builders trained through MongoDB University courses. To learn more, visit mongodb.com.
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Mon, 05 Sep 2022 13:05:00 -0500entext/htmlhttps://www.risk.net/static/risk-learning-risk-certified-facultyAs AI-assisted Software Development Takes the Spotlight, Unlocking EfficiencyNo result found, try new keyword!In a groundbreaking development, the landscape of software development is undergoing a profound transformation with the advent of AI-assisted software development ...Mon, 06 Nov 2023 14:12:47 -0600en-ustext/htmlhttps://www.msn.com/ECTL Certification & Badge Program
Overview
The ECTL Certification and Badge Program offers an opportunity for UW instructors to further develop as informed scholars, teachers, and effective educators. This self-directed certification and badge program is designed to provide opportunities to engage in teaching and learning activities and reflect on those experiences. The flexible nature of the program is designed to work with your goals, schedule, and interests. All UW teaching personnel including Faculty, Academic Professional Lecturers, Adjuncts/Temporary Lecturers, and Graduate Teaching Assistants are welcome to participate in this program.
Benefits of the Program
Define your personal philosophy of teaching and learning
Deepen your knowledge in a range of courses and strategies related to teaching and learning
Specialization in focus areas in order to refine your foundational knowledge and skills
Analysis of your teaching practices through reflective techniques
Implementation of learning theory in the design and delivery of your courses
Encourage continual improvement of your teaching practices through sound methodologies, tools, and reflection
Better prepare future faculty for careers in academia
(Certification Earners)
Focus Areas and Mentors
There are seven focus areas for participants to earn points toward certifications and/or badges. Each area has a mentor who will evaluate reflections for points and assist as the contact person for that area.
In addition to the seven focus areas, there is a Core component that is required to complete the Certification in Teaching Learning. The mentor for the Core isJanel Seeley(jseeley1@uwyo.edu).
Program Options
Participants choose which certification program they want to pursue and/or if they want to earn badges in certain focus areas.Focus areasinclude: Active Learning, Assessment, Communication Pedagogies, Critical & Creative Thinking, Diversity Equity & Inclusion, Information & Digital Literacy, & Online Education.
Option 1:Certification in Teaching and Learning (100 points) Option 2:Certification in one focus area (100 points) Option 3:Badges (Bronze=25 points; Silver=50 points; Gold=75 points)
Note: If you start out with the intention to earn badges and later decide to apply badges toward certification, you can do that!
How to Earn Points
The certification and badge program is managed through a WyoLearn Course shell. Once participants register they will receive information about enrolling in the course shell.
Find and engage in teaching and learning activities that are of interest to you and write a short reflection about these activities (see ideas below).
Determine which focus area you feel most relates to an activity, and post and submit your reflection under that focus area.
Include in your post the number of points you are requesting for the activity. Generally, 1 hour of your time equals 1 point.
The Mentor for each focus area will review, comment, and award points for your reflection and post points in the Gradebook.
Participants keep track of their own points.
Ideas for Teaching & Learning Activities
The ECTL will post announcements in the WyoLearn course shell about possible activities for earning points across all focus areas. Reminder, this is a self-directed program so any teaching and learning activity that you would like to participate in is up to you!
In addition, mentors may post specific activities and resources in the WyoLearn home page for their focus area.
Questions
Thu, 18 Aug 2022 04:35:00 -0500entext/htmlhttps://www.uwyo.edu/ctl/programs/certification-in-teaching-and-learning/index.htmlThought Industries Cognition 2023 Recap: Experience The Business Of Learning
(MENAFN- PR Newswire) Customer, Partner, and Professional Learning Leaders Gather to Discuss How to Adapt, Innovate, and Scale in a Competitive Market
BOSTON, Nov. 7, 2023 /PRNewswire/ -- Thought Industries, the leading external enterprise learning platform for customer, partner and professional training, recently completed its annual enterprise learning conference, COGNITION 2023, a chance for leaders and practitioners in customer, partner and professional learning to immerse themselves in four days of in-depth sessions, fascinating keynote speeches, illuminating roundtables and essential networking. Here are the key events and takeaways from the conference.
Customer Awards Recognize 5 Trailblazers in External Learning
Thought Industries annual Customer Awards have become a powerful and inspirational part of the annual COGNITION conference, drawing attention towards customer success stories across various industries that have used the Thought Industries platform to build and deliver exceptional learning experiences.
Congratulations go to:
Exterro for the Power Launch of their learning platform, including in-person, online and on-demand software training, Educate 360 who won our Business Impact award, highlighting their new revenue stream that has already made more than $80k through in-platform revenues, Enverus for Innovation , using five different learning options for energy professionals, ITI (Industrial Training International) for Learner Experience , increasing subscriptions by 520% in just a single year, and Celonis , winning the Overall Excellence award, after a year focusing on continuous education, localization, and a robust certification program.
Day 1: Three Superb Keynote Speeches
CEO Barry Kelly opened Tuesday with a powerful keynote speech that looked back on a decade of Thought Industries. He shared the story of how the company has grown over the past ten years, founded on two critical pillars, the ultimate respect for the learner, and learning business optimization. With 21M active learners on the platform, 51M course completions, more than 20M certifications earned, and more than $1B managed on the platform, the company has become synonymous with powering the business of learning. In particular, Barry called out Panorama, Thought Industries' multi-tenancy solution that supports B2B learning at scale. 379 of the Fortune 500 currently use Panorama, and customers have used the platform to generate more than $1B in learning license revenue.
Barry then looked to the future and made some specific predictions for where learning experiences are headed. In particular, he pointed to new functionality designed to aid comprehension, and the way that AI will move the needle across all areas, from tools that Strengthen learning delivery, to a leap forward in how content is created, and a true revolution in how the impact of learning is measured.
Donald Clark then took the stage to discuss the future of AI in society and in learning technologies. There's no doubt that AI is a huge opportunity but there are risks. Donald shared how AI will fundamentally change how we learn, why we learn and even what we learn, and provided practical examples of how to think strategically about AI.
The third arm of the keynote tripod was given by Raghu Viswanathan from MongoDB University , discussing their integration with Thought Industries. The session discussed the age-old dilemma: how do you leverage out of the box functionality to achieve scale, while still customizing the experience for multiple audiences that require different content? MongoDB University used the core Thought Industries platform to serve multiple audiences with Panorama, created 16 out-of-the-box and custom integrations, and delivered a personalized, custom learner dashboard using Thought Industries' headless framework, Helium.
Additional Day One Highlights:
Julie Cochrane from Charles River Developmen discussed how they use TI to boost efficiencies through Event creation, Certificate delivery, Learner notifications, Scheduled reports and more. Donna Weber , Customer Success thought leader and author of Onboarding Matters, shared her thoughts on how to create an exceptional customer experience.
Day 2: Thought Industries Announces Expanded AI Capabilities
Wednesday began with some exciting product announcements from Thought Industries Chief Product Officer, Todd Boes, and members of the Product team, focused on how generative AI is going to enhance the TI platform . Barry Kelly commented, "We see the opportunity to provide our customers some new super powers, including time saved creating content and designing courses, the ability to offer near real-time, personalized feedback and content recommendations that will help set a new standard in the creation and delivery of modern, exceptional learning experiences."
Thought Industries will incorporate generative AI into its platform in four important ways:
Accelerating Content Generation: Adding a generative and prompt-based element to make content creation even easier in the Ti platform, accelerating content production timelines for training materials & assessments. Decreasing Operational Friction: AI-powered site design and building will dramatically reduce development and production time. This no-code building functionality means customers can easily optimize web and mobile experiences solely through an AI-powered prompt interface. Personalizing Coaching at Scale: With AI-powered video assessment, all learners can receive speedy feedback and coaching that is truly helpful (including notes on soft skills), while experts and instructors spend their time only in the areas where they will have the most impact. Serving Highly Relevant Content: Thought Industries has always worked to make learning as relevant, personalized and close to the moment of need as possible. Ti's recommendation engine and machine learning algorithms are a critical part of this vision.
Later in the day, Paul Merrylees, VP Content and Product Marketing took the stage to discuss the Power of Partnership. He shared the TI maturity model, which tracks five discrete stages that organizations go through as they become more mature with their customer learning. This correlates with greater business impact and more concrete wins. This imperative to mature and differentiate is now too important to ignore.
Paul's session was a fantastic opportunity to ask the hard questions about what's holding back your own learning function, and discover what's possible with the help of technology like Helium, Thought Industries' own headless framework.
The final spotlight was on Chief Customer Learning Officer at Scaled Agile , Daniel Quick, and his keynote on Learning at Scale. Daniel went through the Four Cornerstones of Scale, and shared how these can be used as guiding principles when looking to scale learning for a global audience. How can you build systems with flexibility in mind from day one? How can you accelerate the flow of work to get more done in less time? What's the best route to meeting diverse needs of global learners? Daniel answered all of these questions and more to provide a blueprint for how organizations can deliver relevant, engaging and impactful learning experience while driving true business value.
Additional Wednesday Highlights
Dave Derington shared his insight on the power of training, during his session, 'How 1+1=3: De-mystifying Outsourced Services ,' "People often hesitate to invest in training, and it often gets cut. However, what many fail to realize is that without proper training, you can't fully leverage the product's potential." Track II enjoyed a roundtable on how winning learner experience can translate to business impact in EMEA, hearing from five education leaders who continually deliver high-impact learning experiences, and their visions for the future. The session was hosted by The Fosway Group and featured learning expertise from Avado, Education & Training Foundation, Celonis, and more.
Day 3: Introducing Project Relay and Understanding Customer Alignment
The first keynote of the day was with Thought Industries' Jill Sawatzky, Chief Customer Officer, and Jon Synnott, Senior Director Customer Success. Discussing customer alignment, the pair introduced Project Relay, encouraging collaboration across departments and facilitating information sharing in the service of customer value. Just like a relay race, each team passes on the baton of information to the next.
The session was a practical discussion of how to harmonize with customers' needs, expectations and goals, and how to build stronger ties with customers by focusing on their definition of success.
Philip Cahill, Thought Industries VP of Customer Experience then followed up to discuss the customer journey, breaking it down into four major parts. First, organizations need to find the right solution, before implementing it, maintaining, and scaling its impact. At its core, this session was also all about customer alignment, and how teams can find a solution that supports the whole business across various departments by understanding the importance of the customer journey.
Additional Thursday Highlights
Assessments is a huge topic, and we loved this panel discussion led by Ben Hartfield from The Hackett Group , discussing what certification means for the industry, and providing a deep dive on all things credentialing. Led by customer education leader, Evan Hall , a panel discussion on AI allowed a rockstar group of experts to discuss how they are already leveraging the benefits of AI, without getting distracted by the bells and whistles. This roundtable shared tips for how to grab hold of AI at scale, and support teams with learning the right skill sets at the right time. The day closed off with an exceptional roundtable with enterprise learning leaders from Google, Mastercard, Dell and Hewlett Packard Enterprise, who discussed how to build and scale a high impact customer learning organization. They discussed technology, the power of AI, and some smart strategies to get that all-important alignment from the C-suite.
About Thought Industries
Thought Industries powers the business of learning with our industry-leading learning technology. We were founded in 2013 around the core belief that online learning experiences should be modern, intuitive, engaging, and scalable. Today, our growing team builds and maintains the only learning solution with completely native tools and integrations that drive higher engagement, learner proficiency, and retention rates for our customers. Headquartered in Boston, Thought Industries has offices across North America and Europe. For more information, visit thoughtindustries and follow us on LinkedIn and Twitter .
SOURCE Thought Industries
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